A Change Detection at an Agricultural Areas Using Georeferenced Multi-Temporal Image Based on Object Based Method and Color Fusion

Document Type : علمی - پژوهشی

Authors

1 M.Sc. of Photogrammetry and Remote Sensing, K.N. Toosi University

2 Associate Prof., Dep. of Photogrammetry and Remote Sensing, K.N. Toosi University

3 Prof. of Photogrammetry and Remote Sensing Dep., K.N. Toosi University

Abstract

This research studies the suitable process of change detection at at an Agricultural areas by focusing on object based method and color fusion. In order to obtain this goal, it is benefit from Landsat7 images. The main idea of offering object based method is a modern algorithm i.e. Double-layer image are combined and An image of the entire layer is formed. Then by selecting suitable parameters a single image is separated in to several parts and by color fusion and object based classification method the changed and unchanged parts are classified. In fact, color fusion is determined by creating different color areas with elementary images that determines changed parts on visual basics and then by using object based classification method and selecting some parts by the user, the total parts of image is determined. Finally, by selecting training samples only one part of image is labeled and its classification is determined and the ultimate map of changes is obtained. Results show that this method is suitable for reducing training samples, increasing exactness (3%-2.5%), speed and increasing information for classification of spatial information and structure and in addition to spectral information it is better than ordinary methods of change detection from comparing 2 multi-temporal images.

Keywords

References

  1. حاج احمدی، س.، 1392، به‌کارگیری توأمان تصاویر ماهواره‌ای و نقشه‌های رقومی موجود به‌منظور تهیة نقشة تغییرات در مناطق شهری، پایان‌نامة کارشناسی ارشد، دانشگاه خواجه نصیرالدین طوسی، دانشکدة مهندسی نقشه‌برداری (ژئودزی و ژئوماتیک).
  2. هداوند، ا.، 1390، استفاده از روش‌های شیءگرا در طبقه‌بندی تصاویر ابرطیفی، پایان‌نامة کارشناسی ارشد، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة مهندسی نقشه‌برداری (ژئودزی و ژئوماتیک).
  3. Blaschke, T., 2009, Object Based Image Analysis for Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, Journal homepage: www.elsevier.com/ locate/ isprsjprs, PP.10–21.
  4. Burges, C., 1998, A Tutorial on Support Vector Machines for Pattern Recognition, In: Data Mining and Knowledge Discovery, Vol. 2, PP. 121–167.
  5. Cacdac, J., 1998 Application of Change Detection Algorithms for Mine Environmental Monitoring. HYPERLINK "http://www.gisdevelopment.net/aars/acrs/1998/ts9006, shtml"
  6. Chen, G., Hay, G.J., Carvalho, L.M.T. & Wulder, M.A., 2012, Object-Based Change Detection, International Journal of Remote Sensing, Vol. 33, No. 14, PP. 4434–4457.
  7. Chen, M., Su, W., Li, L., Zhang, C., Yue, A. & Li, H., 2009, Comparison of Pixel-Based and Object Oriented Knowledge-Based Classification Methods Using SPOT5 Imagery, Wseas Transactions on Information Science and Applications, ISSN: 1790-0832, PP. 477–489.
  8. Cleve, C., Kelly, M., Kearns, F.R. & Moritz, M., 2008, Classification of the Wildland–Urban Interface: A Comparison of Pixel and Object-Based Classifications Using High-Resolution Aerial Photography Computers, Environment and Urban Systems, 32, 317–326.
  9. Doxani, D., Karantzalos, K. & Tsakiri- Strati, M., 2012, Monitoring Urban Changes Based on Scale-Space Filtering and Object-Oriented Classification, International Journal of Applied Earth Observation and Geoinformation, 15, PP. 38–48.
  10. Gamal, L. & Taha, E.L., 2014, Assessment of Urbanization Encroachment over Al-Monib Island Using Fuzzy Post Classification Comparison and Urbanization Metrics, The Egyptian Journal of Remote Sensing and Space Sciences, PP. 135– 147.
  11. Gao, Y., Mas, J.F & Navarrete, A., 2009, The Improvement of an Object-Oriented Classification Using Multi-Temporal MODIS EVI Satellite Data, International Journal of Digital Earth, Vol. 2, Issue 3, September 2009 , PP. 219–236.
  12. Gomez-Chova, L., Camps-Valls, G., Mu Noz-Mar, J. & Calpe, J., 2007, Semisupervised Image Classification with Laplacian Support Vector Machines, IEEE Geoscience and Remote Sensing Letters, Vol. XX, No. Y, 1–5.
  13. Guo, B., Gunn, S.R., Damper, R.I. & Nelson, J.D.B., 2008, Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification, IEEE Transactions on Image Processing, Vol. 17, No. 4, PP. 622–629.
  14. Hao, M., Zhang, H., Li, Z. & Chen, B., 2017, Unsupervised Change Detection Using a Novel Fuzzy C-Means Clustering Simultaneously Incorporating Local and Global Information, Multimedia Tools and Applications, PP. 118.
  15. Janalipour, M. & Mohammadzadeh, A., 2016, Building Damage Detection Using Object-Based Image Analysis and ANFIS from High-Resolution Image (Case Study: BAM Earthquake, Iran), IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), PP. 1937–1945.
  16. Janalipour, M. & Taleai, M., 2017, Building Change Detection after Earthquake Using Multi Criteria Decision Analysis Based on Extracted Information from High Spatial Resolution Satellite Image, International Journal of Remote Sensing, Vol. 38, PP. 82–99.
  17. Li, X. & Yeh, A.G.O., 1998, Principal Component Analysis of Stacked Multi-Temporal Images for the Monitoring of Rapid Urban Expansion in the Pearl River Delta, International Journal of Remote Sensing, 19, 1501–1518.
  18. Mahesh Pal, 2005, Multiclass Approaches for Support Vector Machine Based Land Cover Classification, MapIndia 2005 Conference, 1–16.
  19. Malila, W.A., 1980, Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat, In: LARS Symposia, P. 385.
  20. Martinez, J.A., Martha, T.R., Kerle, N., van Westen, C.J., Jetten, V.G. & Kumar, K.V., 2012, Object-Oriented Analysis of Multi-Temporal Panchromatic Images for Creation of Historical Landslide Inventories, ISPRS Journal of Photogrammetry and Remote Sensing, 67, PP. 105–119.
  21. Navulur, K., 2007, Multispectral Image Analysis Using the Object-Oriented Paradigm, United States of America, CRC Press.
  22. Singh, A., 1989, Digital Change Detection Techniques Using Remotely-Sensed Data, Int. J. Remote Sensing, 10, 989–1003.
  23. Weismiller, R.A., Kristof, S.J., Scholz, D.K., Anuta, P.E., & Momin, S.A., 1977, Change Detection in Coastal Zone Environment, Photogrammetric Engineering and Remote Sensing, 43, 1533–1539.
  24. Ye, S., Chen, D., Yu, J., 2016, A Targeted Change Detection Procedure by Combining Change Vector Analysis and Post Classification Approach, ISPRS Journal of Photogrammetry and Remote Sensing, PP. 115–124.
  25. Zhigao, Y., Qianqing, Q. & Qifeng, Z., 2006, Change Detection in High Spatial Resolution Images Based on Support Vector Machine, In: IEEE International Symposium on Geoscience and Remote Sensing, PP. 225–228.
Iranian Journal of Remote Sensing & GIS
Volume 9, Issue 3 - Serial Number 3
February 2018
Pages 1-14

Files

Share

How to cite

Statistics